The benefits of computer-aided diagnosis in radiology in general, and particularly in mammography are widely recognized. There have been efforts directed toward computer-aided methods that assist the diagnostician to correctly and efficiently identify problem areas detected in a mammography image and to improve the accuracy with which diagnoses are made using this information.
In mammography, it is recognized that early detection of microcalcification structures in the breast can help to diagnose cancer in early stages where treatment offers more hope of success than at more advanced stages. Research shows that calcifications are typically formed from various salts of calcium, magnesium, or phosphorus collected within the breast as a result of secretions within structures that have become thickened and dried. Microcalcification (abbreviated as MCC) structures tend to take the shape of the cavity in which they form so that analysis of their morphology, density, size, and distribution can help determine whether they are benign or malignant.
Calcification structures are detected in X-ray images of the breast, which are provided as digital data for analysis and assessment. Various calcification attributes can be extracted from this data and used to distinguish benign from suspected malignant calcifications. Benign calcifications tend to appear as single spots (rather than clusters) and have a regular shape, while malignant calcifications most often appear in clusters of spots and are of irregular shapes.
Among the characteristics employed by diagnosticians in working with x-ray images of the breast, the following guidelines can be considered:                Large (>1 mm diameter), coarse calcifications are likely to be benign, but malignant MCCs tend to be punctuate, 0.5 mm or smaller;        Single calcifications are more likely to be benign;        Rounded calcifications of equal size are likely to be benign;        Calcifications scattered through both breasts are more likely to be associated with benign disease;        Groups of calcifications of mixed size with irregular shapes are more characteristic of malignant than benign condition;        Clusters of fine calcifications are more likely to signify malignancy;        Rows of fine calcifications within the ducts are likely to signify malignancy;        Short rods of calcification, particularly if they branch, are highly likely to signify malignancy;        Grossly irregular whorled cluster shapes are likely to signify malignancy; and        In malignant calcification clusters, the average distance between calcifications is typically less than 1 mm.        
Employing these characteristics, image analysis methods used in Computer Aided Diagnostics (CAD) systems extract and quantify image data relating to shape, edge character, and intensity at both the spot and cluster level. The shape can be characterized according to its geometric features such as compactness, perimeter, elongation, ratio of moments, and eccentricity. The edge character shows the comparison of the calcification with its background, which can be analyzed by the gradient of the spot boundary and the contrast between the spot and the background. The intensity-based features of the calcification include the mean intensity of a spot as well as the maximum intensity, the deviation of the intensity, the moment, and the like.
The results of CAD analysis serve as an aid to the diagnostician, assisting to highlight areas of particular interest and to eliminate areas that are not suspicious.
In the literature, some standard abbreviations or acronyms are used in the discussion of mammography accuracy, including:                FP—False Positive, an error in which a benign structure is incorrectly identified as malignant;        FN—False Negative, an error in which a malignant structure is incorrectly identified as benign;        TP—True Positive, a result in which a malignant structure is correctly identified; and        
TN—True Negative, a result in which a benign structure is correctly identified.
Microcalcifications can be subtle in appearance. A number of factors can adversely influence the percentage of correct results obtained from the CAD system. Errors can result from factors such as poor image quality, improper positioning of the patient, film variations, scanner performance, obscuration from fibroglandular tissue, and other problems. Because of these difficulties, some view the success rate in correctly identifying and diagnosing microcalcification structures as disappointing.
Some proposals have been made for improving the accuracy of diagnosis for microcalcification detection and classification.
U.S. Pat. No. 4,907,156 entitled “Method And System For Enhancement And Detection Of Abnormal Anatomic Regions In A Digital Image” to Doi et al. is directed to the use of a local gray level threshold that varies with the standard deviation of surrounding pixel values for isolating microcalcifications.
U.S. Pat. No. 5,999,639 entitled “Method and System for Automated Detection of Clustered Microcalcifications from Digital Mammograms” to Rogers et al. relates to a detection and classification sequence including automatic image cropping, filtering including use of a difference of Gaussian filtering enhancement, clustering, and feature computation
U.S. Pat. No. 5,537,485 entitled “Method for Computer-Aided Detection of Clustered Microcalcifications from Digital Mammograms” to Nishikawa et al. describes a cluster filtering method using successively applied thresholds to isolate suspected malignant calcifications from benign structures.
U.S. Pat. No. 6,014,452 entitled “Method and System for Using Local Attention in the Detection of Abnormalities in Digitized Medical Images” to Zhang et al. describes segmentation and thresholding methods used to detect suspicious clustered microcalcification structures.
U.S. Patent Application Publication No. 2003/0165262 by Nishikawa et al. relates to a classification method for structures in a medical image employing a difference of Gaussians filter and various thresholding techniques, followed by the deployment of a feed-forward artificial neural network (ANN) trained to distinguish malignant from benign structures according to combinations of measured characteristics.
U.S. Pat. No. 5,857,030 entitled “Automated Method and System for Digital Image Processing of Radiologic Images Utilizing Artificial Neural Networks” to Gaborski et al. discloses an automated method for detection of microcalcification structures in a medical image using successive processing stages including filtering and segmentation, with a final pattern classification stage using neural network techniques;
An article entitled “Adaptive noise equalization and recognition of microcalcification clusters in mammograms” International Journal of Pattern Recog and Artificial Intelligence, vol 7, 1357-1376 1993, N. Karssemeijer, describes rescaling the digital image to minimize effects of noise.
An article entitled “Local contrast enhancement for the detection of microcalcifications” in Proc 5th Int . Workshop Digital Mammography, pp. 598-604, 2000 by H. Neiber, T. Muller, R. Stotzka describes the use of a local threshold for identifying microcalcification structures, dependent on the difference between local maximum and mean gray levels.
While such methods may have achieved certain degrees of success in their particular applications, there is still need for improvement. The percentage of FN and FP errors is still too high when using conventional CAD systems. Proposed solutions have often tended to focus on ever more sophisticated image processing algorithms for reducing FN and FP errors. However, even using advanced neural networks and other powerful image analysis and decision-making tool may only provide incremental improvement over existing methods.
Thus, there exists a need for an accurate automated method for identifying and assessing clustered structures in a medical image.